Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs

This repository contains the code for the TACL paper: "Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs".

This repository contains experimental software and is published for the sole purpose of giving additional background details on the respective publication.

This project is implemented using the framework OpenNMT-py and the library PyTorch Geometric. Please, refer to their websites for further details on the installation and dependencies.

Environments and Dependencies

python 3.6
PyTorch 1.1.0
PyTorch Geometric 1.3.1
torch-cluster - 1.4.4
torch-scatter - 1.3.1
torch-sparse - 0.4.0
torch-spline-conv - 1.1.0
subword-nmt 0.3.6

Datasets

In our experiments, we use the following datasets: AGENDA and WebNLG.

Preprocess

First, convert the dataset into the format required for the model.

For the AGENDA dataset, run:

./preprocess_AGENDA.sh <dataset_folder>

For the WebNLG dataset, run:

./preprocess_WEBNLG.sh <dataset_folder>

Training

For traning the model using the AGENDA dataset, execute:

./train_AGENDA.sh <graph_encoder> <gpu_id>

For the WebNLG dataset, execute:

./train_WEBNLG.sh <graph_encoder> <gpu_id>

Options for <graph_encoder> are pge, cge, pge-lw or cge-lw.

Examples:

./train_AGENDA.sh 0 pge
./train_WEBNLG.sh 0 cge-lw

Decoding

For decoding, run:

./decode_AGENDA.sh <gpu_id> <model> <nodes_file> <graph_file> <output>
./decode_WEBNLG.sh <gpu_id> <model> <nodes_file> <graph_file> <output>

Example:

./decode_AGENDA.sh 0 model_agenda_cge_lw.pt test-nodes.txt test-graph.txt output-agenda-testset.txt

Trained models

CGE-LW trained on AGENDA training set (download):
- BLEU on AGENDA test set: 18.10, 58.8/29.5/16.4/9.0 (BP=0.804, ratio=0.821, hyp_len=114233, ref_len=139162) (output)
CGE-LW trained on WEBNLG training set (download):
- BLEU on WEBNLG seen test set: 63.68, 89.8/72.8/58.7/47.6 (BP=0.974, ratio=0.975, hyp_len=21984, ref_len=22554) (output)

For more details regading hyperparameters, please refer to OpenNMT-py and PyTorch Geometric.

Contact person: Leonardo Ribeiro, ribeiro@aiphes.tu-darmstadt.de

Citation

@misc{ribeiro2020modeling,
    title={Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs},
    author={Leonardo F. R. Ribeiro and Yue Zhang and Claire Gardent and Iryna Gurevych},
    year={2020},
    eprint={2001.11003},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

xc15071347094/kg2text